Explaning significance
Clearly describe the basic logic of statistical significance to others, differentiating between significance, the size of an effect, and the statistical power of an analysis. Recognize what statistical significance can reveal and cannot reveal about a phenomenon.
K–2 Competencies
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
3–5 Competencies
Describe how "unusual" a result may be compared to an otherwise expected outcome in a given situation. e.g., flipping a coin 10 times and getting 10 heads is highly unlikely
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
6–8 Competencies
Recognize and describe random chance in a given situation, and explain whether a result is unusual by comparing it to what is expected from random chance. e.g., flipping a coin 10 times and getting 8 heads is less common than 5 heads and 5 tails, but still possible due to random chance
Recognize that a unique result may be considered significant if it is substantially different from outcomes in similar situations.
Recognize that a unique result may be considered significant if it falls far from the typical range of outcomes in a visualized distribution of results.
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
9–10 Competencies
Identify situations when distinguishing from random chance is especially important. e.g., medical drug trial, public policy implementation
Describe probability distributions and give real-world examples of how they can represent different types of random events.
Identify and describe a normal distribution as a possible model for random chance that can be used to determine whether a result is statistically significant.
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
11–12 Competencies
Explain the concept of statistical significance (e.g., including its role in distinguishing meaningful results from random chance) in plain language and the limitations of significance testing (e.g., inability to address study design flaws, confounding variables, or real-world validity beyond a narrow model comparison).
Describe how statistical significance tests are constructed, calculated, and interpreted in the context of chosen probability models and/or assumptions.
Identify real-world instances where assessing statistical significance is crucial (e.g., scientific studies to distinguish actual effects from random variation) while also evaluating the significance claims made by others and recognizing situations where statistical significance is necessary but not sufficient for proving a point.
11-12.D.1.4d Differentiate statistical significance, effect size, and statistical power in simple terms with real-world examples, explaining how each addresses distinct questions in research. e.g., whether outcomes could be connected to random chance, the meaningfulness of impacts in context, the suitability of the analysis approach to the specific data and problems
Classroom resources
Data Science Starter Kit Module 4: Drawing Conclusions - Interpreting Problems and Results
Welcome to one of the most critical skills in data science—learning how to draw valid conclusions from data and understanding what those conclusions can and cannot tell us. This module focuses on the thinking skills that separate good data science from misleading claims.🔗
Interpreting Problems and Results isn’t about complex statistical tests or advanced mathematics. It’s about developing the intellectual honesty and critical thinking skills to say, “Based on this data, here’s what we can reasonably conclude, here’s what we’re not sure about, and here’s what we still need to investigate.”
Advanced Competencies
Describe a p-value to without using the language of the "null hypothesis" or "alternative hypothesis."
Identify examples of p-value misuse in the media or academic research.
Classroom resources
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